2008 CombinationalCollaborativeFilte

(Chen et al., 2008) ⇒ Wen-Yen Chen, Dong Zhang, and Edward Y. Chang. (2008). “Combinational Collaborative Filtering for Personalized Community Recommendation.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401909

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Abstract

Rapid growth in the amount of data available on social networking sites has made information retrieval increasingly challenging for users. In this paper, we propose a collaborative filtering method, Combinational Collaborative Filtering (CCF), to perform personalized community recommendations by considering multiple types of co-occurrences in social data at the same time. This filtering method fuses semantic and user information, then applies a hybrid training strategy that combines Gibbs sampling and Expectation-Maximization algorithm. To handle the large-scale dataset, parallel computing is used to speed up the model training. Through an empirical study on the Orkut dataset, we show CCF to be both effective and scalable.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 CombinationalCollaborativeFilte	Edward Y. Chang Wen-Yen Chen Dong Zhang			Combinational Collaborative Filtering for Personalized Community Recommendation		KDD-2008 Proceedings		10.1145/1401890.1401909		2008